Smarter business intelligence systems

ABSTRACT

An embodiment of the invention provides a method and system for analyzing a plurality of reports. More specifically, a change detection module predicts results of future reports based on past reports and identifies a first report that deviates from its predicted results. A dependency analysis module connected to the change detection module at least one report sharing a dependency with the first report by performing a dependency analysis and/or a usage analysis. The dependency analysis labels the first report and at least one second report as sharing a dependency if the second report deviates from its predicted results. The usage analysis labeling the first report and at least one report analyzed by an analyst as sharing a dependency if the report analyzed by the analyst is analyzed in response to the identification of the first report.

This patent application is a continuation application of U.S. patentapplication Ser. No. 13/012,554 filed on Jan. 24, 2011, which is herebyincorporated by reference.

BACKGROUND

The present invention is in the field of methods and computer programproducts for smarter business intelligence systems.

Business intelligence teams use reports for reporting and analytics.Business intelligence reports (BI reports) are often meta-data rich. Alarge amount of manual effort goes into designing the content of BIreports and the navigational structure across these reports. Root causeanalysis involves retrieving the appropriate reports relevant to thecurrent analysis focus.

SUMMARY OF THE INVENTION

An embodiment of the invention provides a method and system foranalyzing a plurality of reports. More specifically, a change detectionmodule predicts results of future reports based on past reports andidentifies a first report that deviates from its predicted results. Adependency analysis module connected to the change detection module atleast one report sharing a dependency with the first report byperforming a dependency analysis and/or a usage analysis. The dependencyanalysis labels the first report and at least one second report assharing a dependency if the second report deviates from its predictedresults. The usage analysis labeling the first report and at least onereport analyzed by an analyst as sharing a dependency if the reportanalyzed by the analyst is analyzed in response to the identification ofthe first report.

The dependency analysis module also performs a fine grained analysis.Specifically, if the dependency analysis is performed, the first reportand the at least one second report are labeled as sharing a dependencyif at least one sub-component of the first report and at least onesub-component of the second report share a dependency. If the usageanalysis is performed, the first report and the at least one reportanalyzed by the analyst are labeled as sharing a dependency if at leastone sub-component of the first report and at least one sub-component ofthe report analyzed by the analyst share a dependency.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present invention is described with reference to the accompanyingdrawings. In the drawings, like reference numbers indicate identical orfunctionally similar elements.

FIG. 1 illustrates a dependency analysis module according to anembodiment of the invention;

FIG. 2 is a diagram illustrating a method for analyzing reportsaccording to an embodiment of the invention;

FIG. 3 is a diagram illustrating BI reports according to an embodimentof the invention; and

FIG. 4 illustrates a computer program product according to an embodimentof the invention.

DETAILED DESCRIPTION

Exemplary, non-limiting, embodiments of the present invention arediscussed in detail below. While specific configurations are discussedto provide a clear understanding, it should be understood that thedisclosed configurations are provided for illustration purposes only. Aperson of ordinary skill in the art will recognize that otherconfigurations may be used without departing from the spirit and scopeof the invention.

An embodiment of the invention performs a dependency analysis, usageanalysis, and/or fine grain analysis. As described more fully below, thedependency analysis finds the dependencies between various reports; and,the usage analysis finds usage patterns of various reports generated inthe past. The fine grain analysis involves the analysis of the reportgeneration structure query language (SQL) to find a root cause at a finelevel.

FIG. 1 illustrates a system 100 for analyzing reports according to anembodiment of the invention. The system 100 includes a BI executionengine 110 that is connected to a BI reporting environment E. The BIreporting environment E includes BI reports (e.g., BI reports R1, R2,R3, R4, R5, R6, R7, and R8). The BI execution engine 110 periodicallyexecutes the BI reports. Specifically, the BI execution engine 110intelligently schedules the next execution of the BI reports and cachesresults of the executed report runs.

The BI execution engine 110 is connected to a change detection module120, which takes snap shots of the BI reports between report executionruns and detects changes between the report snap-shots. In at least oneembodiment, the change detection module 120 uses histogram differencemethods to measure change. In another embodiment, the change detectionmodule 120 predicts result(s) of future BI report runs (also referred toherein as an “expected trend”) based on the snapshots. The changedetection module 120 flags a BI report if the change deviates from theexpected trend (e.g., from a predetermined threshold from the predictedreport results).

The system 100 further includes a dependency analysis module 130connected to the change detection module 120 and the BI reportingenvironment E. The change detection module 120 inputs the predictedresults into the dependency analysis module 130. The dependency analysismodule 130 uses data mining techniques to find dependencies between theBI reports. For example, a dependency is found between BI reports R6,R7, and R8, if when R6 changes unexpectedly (e.g., deviates from itsexpected trend), R7 and R8 also change unexpectedly.

In at least one embodiment of the invention, the dependency analysismodule 130 performs a usage analysis, which analyzes usage of the BIreports in the past by business users and/or domain experts (alsoreferred to herein as “analysts”). For example, in response to anunexpected change in BI report R5, a business user analyzed BI reportsR4, R7, and R8. Thus, usage analysis finds dependencies between the BIreports R5, R4, R7, and R8. These dependencies are added to thedependencies found in the dependency analysis.

In at least one embodiment of the invention, the dependency analysismodule 130 performs a fine grain analysis, which finds dependencies at afiner granularity than the report level. Report generation SQLs containmany inner and sub-queries. The fine grain analysis finds the query planfor the report generation SQL. Specifically, the fine grain analysisfinds each sub-query that project out certain attributes in asub-component of the report generation SQL. All the sub-components ofthe report generation SQL are collected.

Thus, an embodiment of the invention finds dependencies between reportsat the sub-component level. For example, if BI reports R1 and R2 aredependent on each other, the fine grain analysis finds thesub-components in R1 which are dependent on R2, and visa-a-versa. Assuch, the search space for finding component level dependences isreduced because only dependencies across BI reports which have beendependent on each other in the past are examined (e.g., using usageanalysis and/or dependency analysis). This helps to find “hidden” rootcauses corresponding to sub-components for which no report is defined.

More specifically, having knowledge of which sub-components in a report(e.g., subcomponent R1.A of R1 and R2.C of R2) are related/dependent, anembodiment of the invention infers that a change in a sub-component(e.g., R1.A) could have been triggered due to change in adependent/related sub-component (e.g., R2.C). The user is notified witha list of possible “reasons” that could have triggered the changes insub-component R1.A. This can be done despite the fact that there is noexplicit report defined that contains both R1.A and R2.C subcomponents.

At least one embodiment of the invention ranks root causes. For example,when a user requests a root cause analysis for a given BI report (e.g.,R2), all of the BI reports which are dependent on this BI report arescanned (e.g., R3). At least one embodiment checks the status of all ofthe sub-components to see if their results have deviated from theexpected trend. If a significant deviation is found, then a transitiveinvestigation is performed by finding other BI report and/orsub-component deviation(s) transitively. For example, if R3 has deviatedfrom the expected trend by a predetermined threshold (e.g., as set bythe user or manufacturer and/or administrator of the system 100 (e.g.,default settings in the change detection module 120)), then any otherreport dependent on R3 is checked to see if it has also deviated. Thistransitive traversal is performed for a fixed number of steps (i.e., fora predetermined level of dependency from the BI report being analyzed(i.e., in the above example, R2)). Thus, for example, if R3 is dependenton R2, R4 is dependent on R3 (but not R2), and R5 is dependent on R4(but not R2 or R3), then the root cause analysis is performed for 3steps (i.e., levels) when R3, R4, and R5 are analyzed. The level ofdeviation from the expected trend for each BI report and/orsub-component is quantified (e.g., in percentage terms). The BI reportsand/or sub-components are listed and sorted based on the deviation.

The following description provides an example of a root cause analysis.A railway manager looks at a BI report about time performance ofdifferent trains and notices that a set of trains have been delayed inDelhi. The delays could be due to various reasons, including a strike inthe train car cleaning division which could lead to delay in cleaning ofcoaches of some trains headed towards Delhi. Delays could also beattributed to a railway accident in Itarsi, which could lead to delaysof trains travelling to Delhi.

To find the root cause for the delays, the BI report “Delay status oftrains passing through Itarsi” (R1) is examined. In R1, thesub-component “Delay of trains headed towards Delhi” shows significantdeviation (i.e., meets a predetermined threshold of deviation). Atransitive investigation reveals that the BI report “List of trainaccidents by region” (R2) is dependent on R1 but does not showsignificant deviation. However, a sub-component of R2, “List of trainaccidents in Itarsi”, shows significant deviation (e.g., above a levelof deviation above a predetermined threshold) from its expected trend.Accordingly, BI reports R1 and R2 are displayed to the manager aspossible root causes.

FIG. 2 is a diagram illustrating a method for analyzing reports (e.g.,BI reports) according to an embodiment of the invention (e.g., using thesystem 100). Examples of reports include manufacturing reports, deliveryreports, inventory reports, sales reports, transportationdeparture/arrival reports, performance reports, etc.

The results of future reports are predicted based on past reports 210(e.g., via a calculator sub-component of the change detection module120). For example, R1 is a report for “Inventory of Items A, B, and C”.The change detection module 120 predicts the results for the August R1report based on the R1 reports executed (i.e., produced, prepared) inJune and July.

A first report that deviates from its predicted results is identified220 (e.g., via an analyzer sub-component of the change detection module120). In at least one embodiment, a predetermined threshold of deviationfrom a predicted result is set in the change detection module 120. Inthe above example, the prediction for the August R1 report is 200 unitsof items A, B, and C in inventory; and, the actual August R1 reportshows 220 units in inventory. If the predetermined threshold ofdeviation is 20 or more units (or at least 10%), then the changedetection module 120 identifies (e.g., flags) R1 (i.e., the firstreport).

One or more reports sharing a dependency with the first report isidentified 230 by performing a dependency analysis and/or a usageanalysis (e.g., via a first sub-component and second sub-component,respectively, of the dependency analysis module 130). As used herein,“dependency” refers to situations where contents/results of one reportare influenced, affected, and/or are dependent upon contents/results ofanother report, and/or vice versa. For example, if R2 is a report on“Items A, B, and C Sold”, then R1 (report for “Inventory of Items A, B,and C”) and R2 share a dependency (R2 is dependent on R1). If R3 is areport on “Items A, B, and C manufactured”, then R1, R2, and R3 share adependency (R1 and R2 are dependent on R3).

The dependency analysis examines other reports to identify whether otherreports deviate from their predicted results. If at least one secondreport is identified as deviating from its predicted results, then thefirst report and the at least one second report are labeled as sharing adependency. If no other reports are identified as deviating from itspredicted results, then the dependency analysis does not label anyreports as sharing a dependency with the first report. Thus, forexample, if R4, R5, R6, and R7 are identified as deviating from theirrespective predicted results, then R4, R5, R6, and R7 are each labeledas sharing a dependency with one another.

The usage analysis identifies report(s) that are analyzed (e.g., viewed,copied, printed, downloaded, requested) by an analyst (human and/ornon-human) in response to the first report being identified as deviatingfrom its predicted results. If one or more reports are analyzed by ananalyst in response to the identification of the first report, then thefirst report and the report(s) analyzed by the analyst are labeled assharing a dependency. If no reports are identified as being analyzed byan analyst in response to the identification of the first report, thenthe usage analysis does not label any reports as sharing a dependencywith the first report. For example, if an analyst looks at reports R8,R9, and R10 in response to the identification that R1 deviates from itspredicted results, then R1, R8, R9, and R10 are labeled as sharing adependency.

In at least one embodiment of the invention, the dependency analysismodule 130 further performs a fine grain analysis 240. Morespecifically, if the dependency analysis is performed, the dependencyanalysis module 130 examines sub-components of the first report andsub-components of the at least one second report to determine whetherany of the sub-components of the first report share a dependency withany of the sub-components of the at least one second report. In theexample above, R1 is a report of “Inventory of Items A, B, and C”.Examples of sub-components of R1 are inventory of item A, inventory ofitems A and B combined, inventor of item C in January 2010, andinventory of item C in Store Alpha. Fine grain analysis labels the firstreport and the at least one second report as sharing a dependency if atleast one sub-component of the first report and at least onesub-component of the at least one second report share a dependency.

For example, report R8 has a subcomponent S2 (“Units of item A sold byJohn Doe”); and, R9 has a subcomponent S4 (“Units of item A sold by JaneDoe”). Subcomponent S2 and S4 share a dependency because they are bothdependent on a subcomponent (“Units of item A in inventory in Alphastore”) since John and Jane work in Alpha store. In another example, R8has a subcomponent S11 (“Units of item A delivered to Alpha store).Therefore, subcomponent S11 in R8 and subcomponent S4 in R9 share adependency because subcomponent S4 in R9 (“Units of item A sold by JaneDoe”) is dependent upon subcomponent S11 in R8 (“Units of item Adelivered to Alpha store”). None of the reports are labeled if none ofthe sub-components of the first report share a dependency with any ofthe sub-components of the at least one second report. If the dependencyanalysis is not performed, then a fine grain analysis is not performedon sub-components of the at least one second report.

If the usage analysis is performed, then the fine grain analysisexamines sub-components of the first report and sub-components of the atleast one report analyzed by the analyst to determine whether any of thesub-components of the first report share a dependency with any of thesub-components of the at least one report analyzed by the analyst. Thedependency analysis module 130 labels the first report and the at leastone report analyzed by the analyst as sharing a dependency if at leastone sub-component of the first report and at least one sub-component ofthe at least one report analyzed by the analyst share a dependency. Ifno dependency is found between the sub-components of the first reportand the sub-components of the at least one report analyzed by theanalyst, then no reports are labeled. If the usage analysis is notperformed, then a fine grain analysis is not performed on sub-componentsof the at least one report analyzed by the analyst.

As described above, fine grain analysis can find dependencies betweentwo or more reports even if a dependency is not found by the dependencyanalysis or usage analysis. For example, if no dependency is found afterperforming a dependency analysis and/or usage analysis on reports R11and R12, a dependency may still be found when examining thesub-components of R11 and R12 (i.e., via fine grain analysis).

At least one embodiment of the invention performs a root cause analysisfor a report selected by the user of the system 100 (also referred toherein as the “select report”) 250. Examples of the select reportinclude the first report (e.g., identified as deviating from itspredicted value by the change detection module 120), the at least onesecond report (e.g., identified as also deviating from its predictedvalue in the dependency analysis), or the at least one report analyzedby the analyst (identified in the usage analysis).

The root cause analysis analyzes at least one dependent report, whereinthe dependent report shares a dependency with the select report. Forexample, as illustrated in FIG. 3, R2 is analyzed based on itsdependency on R1. The analysis of the at least one dependent reportincludes calculating a deviation for each of the sub-components of thedependent report that deviate from the predicted results for thedependent report. Thus, in the above example, each sub-component of R2(e.g., total number of item A sold, number of item B sold by John Doe,number of item B sold in January) is compared to its respectivepredicted sub-component value and the deviation from the actual andpredicted values is calculated (e.g., raw deviation in units and/orpercentage deviation). The sub-components of the dependent report areranked (e.g., listed in ascending or descending order) based on thecalculated deviations.

In at least one embodiment of the invention, when the at least onedependent report has a threshold deviation from the predicted results ofthe dependent report at least one second dependent report is analyzed,wherein the second dependent report shares a dependency with thedependent report. In at least one embodiment, the second dependentreport also lacks a dependency with the select report. Analysis of theat least one second dependent report calculates the deviation for eachof the sub-components of the second dependent report that deviate fromthe predicted results of the second dependent report. Thus, further tothe example above, R13 is a report on “Items X, Y, and Z Sold”, whichshares a dependency with R2 (“Items A, B, and C Sold”). Eachsub-component of R13 (e.g., number of item X sold, number of item Y soldby Jane Doe, number of item Z sold in January 2009) is compared to itsrespective predicted sub-component value and the deviation from theactual and predicted values is calculated (e.g., raw deviation in unitsand/or percentage deviation). The sub-components of the dependent reportand/or the sub-components of the second dependent report are rankedbased on the calculated deviations.

Multiple levels of dependency from the select report can be examined.For instance, if R20 is the select report, R21 is dependent on R20, R22is dependent on R21 (but not R20), and R23 is dependent on R22 (but notR21 or R20), then the root cause analysis is performed for 3 steps(i.e., levels) when R21, R22, and R23 are analyzed.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment or an embodiment combining softwareand hardware aspects that may all generally be referred to herein as a“circuit,” “module” or “system.” Furthermore, aspects of the presentinvention may take the form of a computer program product embodied inone or more computer readable medium(s) having computer readable programcode embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute with theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Referring now to FIG. 4, a representative hardware environment forpracticing at least one embodiment of the invention is depicted. Thisschematic drawing illustrates a hardware configuration of an informationhandling/computer system in accordance with at least one embodiment ofthe invention. The system comprises at least one processor or centralprocessing unit (CPU) 10. The CPUs 10 are interconnected with system bus12 to various devices such as a random access memory (RAM) 14, read-onlymemory (ROM) 16, and an input/output (I/O) adapter 18. The I/O adapter18 can connect to peripheral devices, such as disk units 11 and tapedrives 13, or other program storage devices that are readable by thesystem. The system can read the inventive instructions on the programstorage devices and follow these instructions to execute the methodologyof at least one embodiment of the invention. The system further includesa user interface adapter 19 that connects a keyboard 15, mouse 17,speaker 24, microphone 22, and/or other user interface devices such as atouch screen device (not shown) to the bus 12 to gather user input.Additionally, a communication adapter 20 connects the bus 12 to a dataprocessing network 25, and a display adapter 21 connects the bus 12 to adisplay device 23 which may be embodied as an output device such as amonitor, printer, or transmitter, for example.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the root terms “include”and/or “have”, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans plus function elements in the claims below are intended to includeany structure, or material, for performing the function in combinationwith other claimed elements as specifically claimed. The description ofthe present invention has been presented for purposes of illustrationand description, but is not intended to be exhaustive or limited to theinvention in the form disclosed. Many modifications and variations willbe apparent to those of ordinary skill in the art without departing fromthe scope and spirit of the invention. The embodiment was chosen anddescribed in order to best explain the principles of the invention andthe practical application, and to enable others of ordinary skill in theart to understand the invention for various embodiments with variousmodifications as are suited to the particular use contemplated.

What is claimed is:
 1. A system for analyzing a plurality of reports,said system comprising: a change detection module on a processor thatpredicts results of future reports based on past reports and identifyinga first report that deviates from its predicted results; a dependencyanalysis module on a processor, said dependency analysis moduleconnected to said change detection module, said dependency analysismodule performs a usage analysis, the usage analysis identifies at leastone report that has previously been analyzed by an a human analyst, thereport that has previously been analyzed by the human analyst havingbeen requested by the human analyst in response to the identifying ofthe first report as deviating from its predicted results, the usageanalysis, subsequent to said identifying at least one report that haspreviously been analyzed by a human analyst, labels the first report andthe at least one report analyzed by the human analyst as sharing adependency based on the fact that said at least one report that haspreviously been analyzed by a human analyst was requested by the humananalyst in response to said identifying of the first report.
 2. Thesystem according to claim 1, wherein said dependency analysis moduleperforms at least one of: identifying at least one sub-component of thefirst report and at least one sub-component of the at least one secondreport that share a dependency when a dependency analysis is performed;and identifying at least one sub-component of the first report and atleast one sub-component of the at least one report analyzed by the humananalyst that share a dependency when the usage analysis is performed. 3.The system according to claim 1, wherein said dependency analysismodule: analyzes at least one dependent report, the dependent reportsharing a dependency with a select report, and calculates a deviationfor each of the sub-components of the dependent report that deviate fromthe predicted results of the dependent report; and ranks thesub-components of the dependent report based on the calculateddeviations.
 4. The system according to claim 3, wherein the selectreport is the first report, the at least one second report, or the atleast one report analyzed by the human analyst.
 5. The system accordingto claim 3, wherein said dependency analysis module: analyzes at leastone second dependent report, the second dependent report sharing adependency with the dependent report, and calculates a deviation foreach of the sub-components of the second dependent report that deviatefrom the predicted results of the second dependent report; and ranks thesub-components of the dependent report and the sub-components of thesecond dependent report based on the calculated deviations.
 6. Acomputer program product for analyzing a plurality of reports, saidcomputer program product comprising: a non-transitory computer readablestorage medium; first program instructions to predict results of futurereports based on past reports; second program instructions to identify afirst report that deviates from its predicted results; and third programinstructions to perform a usage analysis, said usage analysis including:identifying at least one report that has previously been analyzed by ahuman analyst, said report that has previously been analyzed by a humananalyst having been requested by the human analyst in response to saididentifying of the first report, and subsequent to said identifying atleast one report that has previously been analyzed by a human analyst,labeling the first report and the at least one report that haspreviously been analyzed by the human analyst as sharing a dependencybased on the fact that said at least one report that has previously beenanalyzed by a human analyst was requested by the human analyst inresponse to said identifying of the first report, said first programinstructions, said second program instructions, and said third programinstructions are stored on said computer readable storage medium.
 7. Thecomputer program product according to claim 6, further comprising:fourth program instructions to, when a dependency analysis is performed,identify at least one sub-component of the first report and at least onesub-component of the at least one second report that share a dependency;and fifth program instructions to, when the usage analysis is performed,identify at least one sub-component of the first report and at least onesub-component of the at least one report analyzed by the human analystthat share a dependency.
 8. The computer program product according toclaim 6, further comprising: sixth program instructions to analyze atleast one dependent report, the dependent report sharing a dependencywith the select report, the analysis of the at least one dependentreport including calculating a deviation for each of the sub-componentsof the dependent report that deviate from the predicted results of thedependent report; and seventh program instructions to rank thesub-components of the dependent report based on the calculateddeviations.
 9. The computer program product according to claim 8,wherein the select report is the first report, the at least one secondreport, or the at least one report analyzed by the human analyst. 10.The computer program product according to claim 8, further comprising,when the at least one dependent report has a threshold deviation fromthe predicted results of the dependent report: eighth programinstructions to analyze at least one second dependent report, the seconddependent report sharing a dependency with the dependent report, saidanalyzing of the at least one second dependent report includingcalculating a deviation for each of the sub-components of the seconddependent report that deviate from the predicted results of the seconddependent report; and ninth program instructions to rank thesub-components of the dependent report and the sub-components of thesecond dependent report based on the calculated deviations.
 11. A systemfor analyzing a plurality of reports, said system comprising: a changedetection module on a processor that predicts results of future reportsbased on past reports and identifying a first report that deviates fromits predicted results; a dependency analysis module on a processorconnected to said change detection module, said dependency analysismodule performs a usage analysis, the usage analysis identifies at leastone report that has previously been analyzed by a human analyst, thereport that has previously been analyzed by the human analyst havingbeen requested by the human analyst in response to the identifying ofthe first report as deviating from its predicted results, the usageanalysis, subsequent to said identifying at least one report that haspreviously been analyzed by a human analyst, labels the first report andthe at least one report that has previously been analyzed by the humananalyst as sharing a dependency based on the fact that said at least onereport that has previously been analyzed by a human analyst wasrequested by the human analyst in response to said identifying of thefirst report, said dependency analysis module performs at least one of:identifying at least one sub-component of the first report and at leastone sub-component of the at least one second report that share adependency when the dependency analysis is performed; and identifying atleast one sub-component of the first report and at least onesub-component of the at least one report that has previously beenanalyzed by the human analyst that share a dependency when the usageanalysis is performed.
 12. The system according to claim 11, whereinsaid dependency analysis module: analyzes at least one dependent report,the dependent report sharing a dependency with a select report, andcalculates a deviation for each of the sub-components of the dependentreport that deviate from the predicted results of the dependent report;and ranks the sub-components of the dependent report based on thecalculated deviations.
 13. The system according to claim 12, wherein theselect report is the first report, the at least one second report, orthe at least one report analyzed by the human analyst.
 14. The systemaccording to claim 12, wherein said dependency analysis module: analyzesat least one second dependent report, the second dependent reportsharing a dependency with the dependent report, and calculates adeviation for each of the sub-components of the second dependent reportthat deviate from the predicted results of the second dependent report;and ranks the sub-components of the dependent report and thesub-components of the second dependent report based on the calculateddeviations.